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Collective forecasting using Percypt

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Page 1: Collective forecasting using Percypt
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Percypt helps organisations transform collective insight into accurate forecasts

RealtimeCatch new information immediately, with live continuous tracking of

forecasts. Analyse the data to uncover unexpected shocks

and opportunities.

EngagementIncrease information sharing across your network, identify top forecasters and reward

valuable insight.

AccuracyOvercome bias and

groupthink with more accurate collective forecasts.

30% - 80% more accurate.Better data = better

decisions.

Understanding Percypt November 2016

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Collective ForecastingCollective forecasting = the mathematical aggregation of many independent forecasts. IE Different people know different things. Combing diverse insights, increases foresight.

“The capacity to access collective intelligence is considered to be the defining transformative phenomenon of the Digital Age.”

- Huffpost Business, June 2016

Understanding Percypt November 2016

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Collective Forecasts vs. the CIA

The Intelligence Advanced Research Projects Activity (IARPA) of the US Office of the Director of National Intelligence

Ran a 3 year project to collectively forecast questions on geo-political events and risks

A group of 3,000 ordinary citizens, with nothing more than an internet connection, outperformed CIA analysts with access to classified information, by 30% - 80%.

Understanding Percypt November 2016

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Over 100 years of scientific research, and 962 academic publications

A NEW TYPE OF PREDICTION MARKET

The Economistalmanis.com provides a way to harness the wisdom of crowds to predict future events, without the groupthink

WHAT IF THE CROWD FORECAST THE ECONOMY FOR TREASURY?

The MandarinN.Gruen, N.KamperAdvocates for a modern open source approach to key forecasts, that takes advantage of the opportunities of big data technology and crowd forecasting.  Could this end the cycle of “gotcha” embarrassment for politicians and officials?

THE FORECASTING SWEET SPOT BETWEEN MICRO AND MACRO

Harvard Business ReviewE. Yoon, J. Bartlow, T. JoyceFew companies are really good at forecasting, and there can be big penalties for being wrong. Only 1% of companies hit their financial forecasts over three years, and only one out of five are within 5%. Overall companies were off by 13%, which impacted shareholder value by 6%.

CORPORATE PREDICTION MARKETS: EVIDENCE FROM GOOGLE, FORD, AND FIRM X*

The Review of Economics StudiesB. Cowgill, E. ZitzewitzFinds that corporate prediction markets work better than alternative forecasting methods by up to 25%  

THE WISDOM OF YOUR IN-HOUSE CROWD

Harvard Business ReviewT. DavenportHighlights the value of multiple perspectives and collecting extensive information to good organisational judgment 

CORPORATE PREDICTION MARKETS FOR INNOVATION MANAGEMENT

Otto-Friedrich-University BambergC. F. Horn,  B. S. IvensDeals with the practical use of prediction markets in business and innovation-driven environments. With a model for its use across the innovation process 

Understanding Percypt November 2016

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Commercial Examples• Google: over 1,5000 staff forecasting the likely

success of products eg: how many users will gmail have?

• Intel: collective forecasts outperformed usual mechanisms for allocation of manufacturing capacity

• HP: forecast printer sales more accurately than HP’s official forecasts 75% of the time over 3 years

• Eli Lilly: forecast drug development success with 100% accuracy

• Siemens: predicted project completion dates more reliably than official forecasts

• General Electric: an ideas market to generate business and technology innovations

Understanding Percypt November 2016

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Easy as 1, 2, 3

1. Ask Your QuestionsAsk the questions that matter to your organisation. Eg: What will the market opening price of milk be for the 2016/17 season?

2. Invite your TeamInvite who you want to contribute to your private Percypt platform. The more diversity the better to capture different perspectives.Eg: Dairy commodities traders worldwide

3. Get Forecasting!Each user starts with 1,000 points, they choose to invest points to make a forecast. How many points depends on how far they want to move the forecast.Users share, and debate information and rationales using the comment thread.

Understanding Percypt November 2016

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Understanding Percypt November 2016

1

2

3

4

1. The current collective forecast

2. Forecast whether you think it is more or less likely

3. Choose how many points to invest

4. Review your return

The Market Mechanism

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A League Above Surveys

While both surveys and collective forecasts elicit the views of a group; surveys face serious shortcomings that limits the engagement of respondents and the quality of their results.

• Quality: Survey respondents often answer every question regardless of confidence. On Percypt users chose which questions to forecast on and how many points they invest indicates their confidence

• Incentives: surveys reward participation not accuracy; offering respondents little incentive to give their best answer or do additional research.

• Real-time Feedback: results are live and continuously updated to reflect new market information, opposed to static results analysis once the survey is closed

• Interactivity: surveys do not enable debate, discourse and the sharing of information between engaged users to optimise collective intelligence

Understanding Percypt November 2016

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The Benefits

Synthesis of often competing ideas and expert opinions into a coherent, probability signal• More accurate, lower cost forecasts• Real-time, continuous data• Interactive engagement of wide community • Eliminates bias and group think• Rewards informational value• Measurement and auditing of forecast accuracy

Understanding Percypt November 2016

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Accuracy

Almanis is our global public forecasting site• 170-200 questions live at

any time• 1620 users,

(4100+ registrations)• 42,687 forecasts• Over our first 169 closed

questions the Brier score* median for final crowd forecast was 0.0059.

*Brier scores are the mathematical measure of forecast accuracy. A random guess = 0.5, typical benchmark = 0.4, meteorologists = 0.2

Understanding Percypt November 2016